Currently many applications facilitate form filling such as, but are not limited to, bank account opening, mobile connection, electricity connection, health or insurance claim forms digitization by manually filling each field in the respective forms or applying OCR techniques. The processing of such hand-filled forms is usually performed in the back office of respective organizations, which may be costly in terms of both time and money. In addition, various state of the art processing techniques determines the completeness of the form by checking if there is any content filled in one or more text fields of the hand-filled form. However, the existing processing techniques have the limitation that it assumes the form to be complete and correct even if there is a scribble or wrongly filled in the one or more text fields.
In addition, during the auto-population of hand-written content in the forms in the back-office transcription scenarios, for a given hand-filled form, a data-entry operator either fills the whole content of the form manually or gets a set of options to choose from. The latter approach is an automatic way but it is very hard to recommend a correct word based on a word-image, especially for the hand-written content. Therefore, even if one character is incorrectly recognized, the data-entry operator usually deletes the whole recommendation and types it afresh. Thus this requires at least as many keystrokes (if not more for using backspace to delete the recommendation) and time as it would take in manual data entry.